{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ResSimulation_1D_PressureResponse.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM3Gj12dloYdQYhxD5R/7b4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/ResSimulation_1D_PressureResponse.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nbVSiRzJxGK8",
        "colab_type": "text"
      },
      "source": [
        "#1-D Block Centered Grid Based Reservoir Simulation\n",
        "\n",
        "divyanshu vyas | dvyas13ad@gmail.com\n",
        "\n",
        "Courtsey - T. Ertekin, Abou Kassem et al."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8Fu-8JowAU7",
        "colab_type": "text"
      },
      "source": [
        "![Block-Centered Grid System](https://drive.google.com/uc?export=view&id=1jda8xs-TCi5NRMHunxsGFOi2Gzm4v_QH)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kq48WcOY1BS1",
        "colab_type": "text"
      },
      "source": [
        "## BOUNDARY CONDITIONS - No flow boundaries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Se7g-k2XyFp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_fjAYF6cbhb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Initialization\n",
        "\n",
        "# Pi = 6000\n",
        "\n",
        "dx = 1000\n",
        "dy = 1000\n",
        "dz = 75\n",
        "\n",
        "B = 1 #RB/STB\n",
        "c = 3.5*(10**(-6)) #psi-1\n",
        "\n",
        "kx = 15 #mD\n",
        "phi= 0.18\n",
        "\n",
        "mu = 10 #cp\n",
        "\n",
        "dt = 10 #days\n",
        "\n",
        "#Well Block index wb\n",
        "\n",
        "wb = 3 # 0,1,2,3 (block 4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BtcD1bgFdU5A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Calclulation\n",
        "\n",
        "Ax = dy*dz #ft2\n",
        "\n",
        "Vb = dx*dy*dz #ft3\n",
        "\n",
        "qsc_wb = -150 #stb/d\n",
        "\n",
        "Tx = (0.001127)*(kx)*(Ax)/(mu*B*dx)\n",
        "\n",
        "M = 5.615*B*dt/Vb/phi/c\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rwGuF4w3zr6H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b1d3a72c-5770-4052-db6c-3569474788a0"
      },
      "source": [
        "#Creating a grid-block skeleton using numpy arrays.\n",
        "\n",
        "# P = np.ones(5)\n",
        "\n",
        "# P"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 1., 1., 1., 1.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dO3qNFkdvv2W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Initial Pressure in all Grid blocks - \n",
        "#Initial Time step means n=0\n",
        "\n",
        "# P_resp= []\n",
        "\n",
        "# P = P*6000 #psi\n",
        "\n",
        "# P_resp.append(P)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tPyo8mYAx25B",
        "colab_type": "text"
      },
      "source": [
        "Note that the space index varies from [0,5) as per python Nomenclature. \n",
        "\n",
        "Time steps vary from t = 0 days to t = 360 days."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aq6hZpeK5zbw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3f1f8fef-5f4c-4519-a9f9-02416c92c906"
      },
      "source": [
        "t = np.arange(0,370,dt)\n",
        "# t\n",
        "len(t)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCXGvWRavxAs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8e43fbee-1666-4599-e84e-692efecc655c"
      },
      "source": [
        "# P_resp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([6000., 6000., 6000., 6000., 6000.])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5YvY8vt0NRH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# | 0 | 1 | 2 | 3^ | 4 | ..... where 3^ means there's a production well in block index 3."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4KFiZhW0dJR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# P = np.ones(5)\n",
        "# P = P*6000 #psi\n",
        "\n",
        "# #For n = 1 ie at the end of 10 days \n",
        "\n",
        "# qsc_0124 = 0\n",
        "# qsc_3 = -150\n",
        "\n",
        "# #GridBlock 0\n",
        "# P[0] = P[0] + M*qsc_0124 + M*(Tx*(P[1]-P[0]))\n",
        "\n",
        "# #Gridblock 1\n",
        "# P[1] = P[1] + M*qsc_0124 + M*( Tx*(P[0] - P[1]) + Tx*(P[2] - P[1]) )\n",
        "\n",
        "# #Gridblock 2\n",
        "# P[2] = P[2] + M*qsc_0124 + M*( Tx*(P[1] - P[2]) + Tx*(P[3] - P[2]) )\n",
        "\n",
        "# #GridBlock 4\n",
        "# P[4] = P[4] + M*qsc_0124 + M*(Tx*(P[3]-P[4]))\n",
        "\n",
        "# #Gridblock 3\n",
        "# P[3] = P[3] + M*qsc_3 + M*( Tx*(P[2] - P[3]) + Tx*(P[4] - P[3]) )\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# #So, when you do this step by step, seperately. Always execute and calculate \n",
        "# # the well block at the end.\n",
        "\n",
        "# #So this same procedure we can conduct for the entire time domain."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VlkPeah2VHG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# P = np.ones(5)\n",
        "# P = P*6000 #psi\n",
        "\n",
        "# P_resp= []\n",
        "\n",
        "# P_resp.append(P)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dRyzYQh2hiq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# P"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Gf2UdcP6unU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# list(range(len(P)))\n",
        "# list(range(1,len(P)-1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxDFrsMR66EV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# P"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwGFK6arBU97",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b16fcf11-9dd7-4297-90f3-2a076703ef8c"
      },
      "source": [
        "P = np.ones(5)\n",
        "P = P*6000 #psi\n",
        "P"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6000., 6000., 6000., 6000., 6000.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSQQIAJXEune",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f772f7fe-b878-4b9a-ae34-29cdcd44b689"
      },
      "source": [
        "# M*Tx"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1506691666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EMSlzp-WOziO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "P_r = np.zeros((37,5))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlaC_YnRPIiQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "outputId": "a99676c1-8a85-4e74-e008-16070b09915e"
      },
      "source": [
        "P_r[0] = np.ones(5)*6000\n",
        "\n",
        "P_r"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6000., 6000., 6000., 6000., 6000.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.],\n",
              "       [   0.,    0.,    0.,    0.,    0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCAmaRt5QYKR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ffcfd411-18dc-41b1-fa65-16086c0f88ff"
      },
      "source": [
        "# P_r[4,4]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iNoHS9z5lPE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# P = np.ones(5)\n",
        "# P_o = P*6000 #psi\n",
        "\n",
        "wb = 3 #can ask user input as well.\n",
        "qsc = np.array([0,0,0,-150,0])\n",
        "# T = np.array([0,Tx,Tx,Tx,Tx,0])\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "for j in range(1,len(t)):\n",
        "  \n",
        "  # P[0] = P[0] + M*qsc[0] + M*(Tx*(P[1] - P[0]))\n",
        "  # P[-1] = P[-1] + M*qsc[-1] + M*(Tx*(P[-2] - P[-1]))\n",
        "\n",
        "  for i in range(1, len(P)-1):\n",
        "    P_r[j,i] = P_r[j-1,i] + M*qsc[i] + M*(Tx*(P_r[j-1,i-1] - P_r[j-1,i]) + Tx*(P_r[j-1,i+1] - P_r[j-1,i]))\n",
        "  \n",
        "  \n",
        "  P_r[j,0] = P_r[j-1,0] +  M*qsc[0] + M*(Tx*(P_r[j-1,1] - P_r[j-1,0]))\n",
        "  P_r[j,-1]= P_r[j-1,-1] +  M*qsc[-1] + M*(Tx*(P_r[j-1,-2] - P_r[j-1,-1]))\n",
        "\n",
        "  \n",
        "\n",
        "  \n",
        "  # P[0] = P[0] + M*qsc[0] + M*(Tx*(P[1] - P[0]))\n",
        "  # P[] = P[-1] + M*qsc[-1] + M*(Tx*(P[-2] - P[-1]))\n",
        "\n",
        "  # P_resp.append(P)\n",
        "\n",
        "    \n",
        "\n",
        "    \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bf4aH9109AXI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "P_response = pd.DataFrame(P_r)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cAdoFIsuSrur",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp = P_response.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "leBd_1rE9CRr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp['Time(days)'] = t"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJhpAD2LSRCR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp = temp.drop('Time',axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gNB9K6FPTc9W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp.columns = ['0' , '1' , '2' , '3' , '4' , 'Time(days)']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-HZwXGdTGy1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "78ed2ecf-6696-4cba-dee4-4c29f5759929"
      },
      "source": [
        "plt.style.use('default')\n",
        "\n",
        "# plt.title('Blockwise Pressure Variation')\n",
        "\n",
        "plt.figure(figsize=(10,4))\n",
        "\n",
        "plt.plot(temp['Time(days)'],temp['0'], label = 'Pr. Variation in Block #1',marker='o')\n",
        "plt.plot(temp['Time(days)'],temp['1'], label = 'Pr. Variation in Block #2',marker='x')\n",
        "plt.plot(temp['Time(days)'],temp['2'], label = 'Pr. Variation in Block #3',marker='D')\n",
        "plt.plot(temp['Time(days)'],temp['3'], label = 'Pr. Variation in Well-Block(4)',marker='*')\n",
        "plt.plot(temp['Time(days)'],temp['4'], label = 'Pr. Variation in Block #5',marker='s')\n",
        "\n",
        "\n",
        "plt.xlabel('Time(days)')\n",
        "plt.ylabel('Pressure(psia)')\n",
        "plt.title('Blockwise Pressure Variation')\n",
        "\n",
        "\n",
        "plt.legend(loc='best')\n",
        "plt.grid()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3GJ78tVT41R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "dbe97b77-ebf2-43ef-d5ca-fd2bae272c98"
      },
      "source": [
        "temp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>Time(days)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>5821.746032</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6000.000000</td>\n",
              "      <td>6000.000000</td>\n",
              "      <td>5973.142623</td>\n",
              "      <td>5697.206817</td>\n",
              "      <td>5973.142623</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6000.000000</td>\n",
              "      <td>5995.953421</td>\n",
              "      <td>5935.614184</td>\n",
              "      <td>5602.102885</td>\n",
              "      <td>5931.567605</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5999.390305</td>\n",
              "      <td>5987.471853</td>\n",
              "      <td>5894.455577</td>\n",
              "      <td>5523.738961</td>\n",
              "      <td>5881.927430</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5997.594562</td>\n",
              "      <td>5975.252912</td>\n",
              "      <td>5852.614698</td>\n",
              "      <td>5455.308514</td>\n",
              "      <td>5827.959472</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5994.228364</td>\n",
              "      <td>5960.141312</td>\n",
              "      <td>5811.230704</td>\n",
              "      <td>5393.063347</td>\n",
              "      <td>5771.812463</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5989.092497</td>\n",
              "      <td>5942.840943</td>\n",
              "      <td>5770.662014</td>\n",
              "      <td>5334.880120</td>\n",
              "      <td>5714.746649</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5982.123813</td>\n",
              "      <td>5923.867570</td>\n",
              "      <td>5730.945175</td>\n",
              "      <td>5279.519220</td>\n",
              "      <td>5657.512476</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5973.346394</td>\n",
              "      <td>5903.577533</td>\n",
              "      <td>5691.996659</td>\n",
              "      <td>5226.233153</td>\n",
              "      <td>5600.560547</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>5962.834378</td>\n",
              "      <td>5882.210835</td>\n",
              "      <td>5653.699174</td>\n",
              "      <td>5174.554980</td>\n",
              "      <td>5544.160950</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>5950.686896</td>\n",
              "      <td>5859.928656</td>\n",
              "      <td>5615.936579</td>\n",
              "      <td>5124.181492</td>\n",
              "      <td>5488.472727</td>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>5937.012427</td>\n",
              "      <td>5836.841041</td>\n",
              "      <td>5578.606333</td>\n",
              "      <td>5074.907310</td>\n",
              "      <td>5433.585270</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>5921.919688</td>\n",
              "      <td>5813.025772</td>\n",
              "      <td>5541.622429</td>\n",
              "      <td>5026.586963</td>\n",
              "      <td>5379.543561</td>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5905.512733</td>\n",
              "      <td>5788.540612</td>\n",
              "      <td>5504.914580</td>\n",
              "      <td>4979.112636</td>\n",
              "      <td>5326.363884</td>\n",
              "      <td>140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>5887.888641</td>\n",
              "      <td>5763.431006</td>\n",
              "      <td>5468.426137</td>\n",
              "      <td>4932.400864</td>\n",
              "      <td>5274.043828</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>5869.136713</td>\n",
              "      <td>5737.734796</td>\n",
              "      <td>5432.111794</td>\n",
              "      <td>4886.384438</td>\n",
              "      <td>5222.568767</td>\n",
              "      <td>160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>5849.338495</td>\n",
              "      <td>5711.485050</td>\n",
              "      <td>5395.935471</td>\n",
              "      <td>4841.007368</td>\n",
              "      <td>5171.916155</td>\n",
              "      <td>170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>5828.568232</td>\n",
              "      <td>5684.711722</td>\n",
              "      <td>5359.868508</td>\n",
              "      <td>4796.221706</td>\n",
              "      <td>5122.058404</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>5806.893491</td>\n",
              "      <td>5657.442606</td>\n",
              "      <td>5323.888171</td>\n",
              "      <td>4751.985475</td>\n",
              "      <td>5072.964860</td>\n",
              "      <td>190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>5784.375851</td>\n",
              "      <td>5629.703878</td>\n",
              "      <td>5287.976437</td>\n",
              "      <td>4708.261306</td>\n",
              "      <td>5024.603163</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>5761.071554</td>\n",
              "      <td>5601.520386</td>\n",
              "      <td>5252.119030</td>\n",
              "      <td>4665.015497</td>\n",
              "      <td>4976.940199</td>\n",
              "      <td>210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>5737.032112</td>\n",
              "      <td>5572.915816</td>\n",
              "      <td>5216.304641</td>\n",
              "      <td>4622.217364</td>\n",
              "      <td>4929.942765</td>\n",
              "      <td>220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>5712.304847</td>\n",
              "      <td>5543.912773</td>\n",
              "      <td>5180.524315</td>\n",
              "      <td>4579.838760</td>\n",
              "      <td>4883.578035</td>\n",
              "      <td>230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>5686.933353</td>\n",
              "      <td>5514.532830</td>\n",
              "      <td>5144.770959</td>\n",
              "      <td>4537.853727</td>\n",
              "      <td>4837.813892</td>\n",
              "      <td>240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>5660.957910</td>\n",
              "      <td>5484.796561</td>\n",
              "      <td>5109.038959</td>\n",
              "      <td>4496.238221</td>\n",
              "      <td>4792.619144</td>\n",
              "      <td>250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>5634.415826</td>\n",
              "      <td>5454.723560</td>\n",
              "      <td>5073.323867</td>\n",
              "      <td>4454.969896</td>\n",
              "      <td>4747.963677</td>\n",
              "      <td>260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>5607.341742</td>\n",
              "      <td>5424.332470</td>\n",
              "      <td>5037.622163</td>\n",
              "      <td>4414.027934</td>\n",
              "      <td>4703.818548</td>\n",
              "      <td>270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>5579.767888</td>\n",
              "      <td>5393.641005</td>\n",
              "      <td>5001.931060</td>\n",
              "      <td>4373.392899</td>\n",
              "      <td>4660.156038</td>\n",
              "      <td>280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>5551.724305</td>\n",
              "      <td>5362.665976</td>\n",
              "      <td>4966.248350</td>\n",
              "      <td>4333.046615</td>\n",
              "      <td>4616.949675</td>\n",
              "      <td>290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>5523.239044</td>\n",
              "      <td>5331.423324</td>\n",
              "      <td>4930.572286</td>\n",
              "      <td>4292.972062</td>\n",
              "      <td>4574.174237</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>5494.338330</td>\n",
              "      <td>5299.928147</td>\n",
              "      <td>4894.901483</td>\n",
              "      <td>4253.153285</td>\n",
              "      <td>4531.805740</td>\n",
              "      <td>310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>5465.046709</td>\n",
              "      <td>5268.194737</td>\n",
              "      <td>4859.234847</td>\n",
              "      <td>4213.575316</td>\n",
              "      <td>4489.821407</td>\n",
              "      <td>320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>5435.387187</td>\n",
              "      <td>5236.236614</td>\n",
              "      <td>4823.571509</td>\n",
              "      <td>4174.224100</td>\n",
              "      <td>4448.199638</td>\n",
              "      <td>330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>5405.381336</td>\n",
              "      <td>5204.066557</td>\n",
              "      <td>4787.910784</td>\n",
              "      <td>4135.086431</td>\n",
              "      <td>4406.919972</td>\n",
              "      <td>340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>5375.049406</td>\n",
              "      <td>5171.696644</td>\n",
              "      <td>4752.252126</td>\n",
              "      <td>4096.149897</td>\n",
              "      <td>4365.963039</td>\n",
              "      <td>350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>5344.410415</td>\n",
              "      <td>5139.138279</td>\n",
              "      <td>4716.595106</td>\n",
              "      <td>4057.402826</td>\n",
              "      <td>4325.310518</td>\n",
              "      <td>360</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              0            1            2            3            4  Time(days)\n",
              "0   6000.000000  6000.000000  6000.000000  6000.000000  6000.000000           0\n",
              "1   6000.000000  6000.000000  6000.000000  5821.746032  6000.000000          10\n",
              "2   6000.000000  6000.000000  5973.142623  5697.206817  5973.142623          20\n",
              "3   6000.000000  5995.953421  5935.614184  5602.102885  5931.567605          30\n",
              "4   5999.390305  5987.471853  5894.455577  5523.738961  5881.927430          40\n",
              "5   5997.594562  5975.252912  5852.614698  5455.308514  5827.959472          50\n",
              "6   5994.228364  5960.141312  5811.230704  5393.063347  5771.812463          60\n",
              "7   5989.092497  5942.840943  5770.662014  5334.880120  5714.746649          70\n",
              "8   5982.123813  5923.867570  5730.945175  5279.519220  5657.512476          80\n",
              "9   5973.346394  5903.577533  5691.996659  5226.233153  5600.560547          90\n",
              "10  5962.834378  5882.210835  5653.699174  5174.554980  5544.160950         100\n",
              "11  5950.686896  5859.928656  5615.936579  5124.181492  5488.472727         110\n",
              "12  5937.012427  5836.841041  5578.606333  5074.907310  5433.585270         120\n",
              "13  5921.919688  5813.025772  5541.622429  5026.586963  5379.543561         130\n",
              "14  5905.512733  5788.540612  5504.914580  4979.112636  5326.363884         140\n",
              "15  5887.888641  5763.431006  5468.426137  4932.400864  5274.043828         150\n",
              "16  5869.136713  5737.734796  5432.111794  4886.384438  5222.568767         160\n",
              "17  5849.338495  5711.485050  5395.935471  4841.007368  5171.916155         170\n",
              "18  5828.568232  5684.711722  5359.868508  4796.221706  5122.058404         180\n",
              "19  5806.893491  5657.442606  5323.888171  4751.985475  5072.964860         190\n",
              "20  5784.375851  5629.703878  5287.976437  4708.261306  5024.603163         200\n",
              "21  5761.071554  5601.520386  5252.119030  4665.015497  4976.940199         210\n",
              "22  5737.032112  5572.915816  5216.304641  4622.217364  4929.942765         220\n",
              "23  5712.304847  5543.912773  5180.524315  4579.838760  4883.578035         230\n",
              "24  5686.933353  5514.532830  5144.770959  4537.853727  4837.813892         240\n",
              "25  5660.957910  5484.796561  5109.038959  4496.238221  4792.619144         250\n",
              "26  5634.415826  5454.723560  5073.323867  4454.969896  4747.963677         260\n",
              "27  5607.341742  5424.332470  5037.622163  4414.027934  4703.818548         270\n",
              "28  5579.767888  5393.641005  5001.931060  4373.392899  4660.156038         280\n",
              "29  5551.724305  5362.665976  4966.248350  4333.046615  4616.949675         290\n",
              "30  5523.239044  5331.423324  4930.572286  4292.972062  4574.174237         300\n",
              "31  5494.338330  5299.928147  4894.901483  4253.153285  4531.805740         310\n",
              "32  5465.046709  5268.194737  4859.234847  4213.575316  4489.821407         320\n",
              "33  5435.387187  5236.236614  4823.571509  4174.224100  4448.199638         330\n",
              "34  5405.381336  5204.066557  4787.910784  4135.086431  4406.919972         340\n",
              "35  5375.049406  5171.696644  4752.252126  4096.149897  4365.963039         350\n",
              "36  5344.410415  5139.138279  4716.595106  4057.402826  4325.310518         360"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fuwh36ZDWOZx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}